EDBT 2026 Demo / reviewers in the wild / expert
Walter Litwinczyk
dblp:181/3957
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Human-robot interaction · 50% Wearable and physiological sensing · 50% | |
| Artificial intelligence
1 paper |
Robot manipulation · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wearable and physiological sensing
eye tracking |
0.2 | 1 | 2016 | Visual cues used to evaluate grasps from images · ICRA 2016 |
Human-robot interaction › nonverbal communication
visual cues |
0.2 | 1 | 2016 | Visual cues used to evaluate grasps from images · ICRA 2016 |
Robotics › Robot manipulation
grasping |
0.1 | 1 | 2016 | Visual cues used to evaluate grasps from images · ICRA 2016 |
Robotics › Robot manipulation › grasping › grasp quality evaluation
grasp success prediction |
0.1 | 1 | 2016 | Visual cues used to evaluate grasps from images · ICRA 2016 |
Methods — techniques the papers use, named apart from their topics
transition matrix · 0.2transition matrices · 0.2eye-tracking · 0.2eye tracking · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Visual cues used to evaluate grasps from imagesabstractWe analyze visual cues people used to evaluate a robot grasp. Participants were presented with two (front and side) orthogonal views of a robot hand grasping an object and asked how successful the grasp would be on a scale of 1-5; they were eye-tracked while completing this survey. Ground truth of the success of the grasps is known. Our primary observations were that (1) Most of the failed grasp predictions were false positives, and this was exacerbated for grasps that were ranked as human-like. (2) Two visual cues from human-grasp research (object center-line and top) were used, but not contact points. Instead, participants gazed at robot finger, wrist, and arm locations. (3) There was a difference in the visual patterns between the left and right images, indicating that the second image was primarily used to verify the locations of fingers and wrist while the first was used to establish the object's location and shape. Finally, we generate transition matrices to model the temporal aspect of the gaze patterns. Matthew Sundberg, Walter Litwinczyk, Cindy Grimm, Ravi Balasubramanian |
ICRA | 2 |